The future is big graphs

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?

[1]  Vito Giovanni Castellana,et al.  In-Memory Graph Databases for Web-Scale Data , 2015, Computer.

[2]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[3]  Jeremy G. Siek,et al.  The Boost Graph Library - User Guide and Reference Manual , 2001, C++ in-depth series.

[4]  Stefan Plantikow,et al.  Cypher: An Evolving Query Language for Property Graphs , 2018, SIGMOD Conference.

[5]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[6]  Alexandru Iosup,et al.  The AtLarge Vision on the Design of Distributed Systems and Ecosystems , 2019, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).

[7]  Jeremy Avigad,et al.  A Machine-Checked Proof of the Odd Order Theorem , 2013, ITP.

[8]  Danai Koutra,et al.  Graph Summarization Methods and Applications: A Survey , 2016 .

[9]  Felix Conrads,et al.  How Representative Is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks , 2019, WWW.

[10]  Cristina L. Abad,et al.  Methodological Principles for Reproducible Performance Evaluation in Cloud Computing SPEC RG Cloud Working Group , 2019 .

[11]  Martin Nöllenburg,et al.  Guidelines for Experimental Algorithmics: A Case Study in Network Analysis , 2019, Algorithms.

[12]  Alexandru Iosup,et al.  Exploring HPC and Big Data Convergence: A Graph Processing Study on Intel Knights Landing , 2018, 2018 IEEE International Conference on Cluster Computing (CLUSTER).

[13]  Amine Mhedhbi,et al.  The ubiquity of large graphs and surprising challenges of graph processing: extended survey , 2017, The VLDB Journal.

[14]  Irena Holubová,et al.  Graph Generators: State of the Art and Open Challenges. , 2020 .

[15]  Jiawei Han,et al.  On graph query optimization in large networks , 2010, Proc. VLDB Endow..

[16]  Christos Faloutsos,et al.  Sampling from large graphs , 2006, KDD '06.

[17]  Alfred V. Aho,et al.  Universality of data retrieval languages , 1979, POPL.

[18]  Marcelo Arenas,et al.  Foundations of Modern Query Languages for Graph Databases , 2016, ACM Comput. Surv..

[19]  Juan Sequeda,et al.  G-CORE: A Core for Future Graph Query Languages , 2017, SIGMOD Conference.

[20]  Emilio Jesús Gallego Arias,et al.  Certified Graph View Maintenance with Regular Datalog , 2018, Theory and Practice of Logic Programming.

[21]  R. V. van Nieuwpoort,et al.  The Grid 2: Blueprint for a New Computing Infrastructure , 2003 .

[22]  M. Tamer Özsu,et al.  Regular Path Query Evaluation on Streaming Graphs , 2020, SIGMOD Conference.

[23]  Alexandru Iosup,et al.  LDBC Graphalytics: A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms , 2016, Proc. VLDB Endow..

[24]  E. F. CODD,et al.  A relational model of data for large shared data banks , 1970, CACM.

[25]  George H. L. Fletcher,et al.  Querying Graphs , 2018, Querying Graphs.

[26]  Ambuj K. Singh,et al.  Graphs-at-a-time: query language and access methods for graph databases , 2008, SIGMOD Conference.

[27]  Vladimir Vlassov,et al.  High-Level Programming Abstractions for Distributed Graph Processing , 2016, IEEE Transactions on Knowledge and Data Engineering.

[28]  Ashok K. Chandra Theory of database queries , 1988, PODS '88.

[29]  F. E.,et al.  A Relational Model of Data Large Shared Data Banks , 2000 .

[30]  Alexandru Iosup,et al.  Massivizing Computer Systems: A Vision to Understand, Design, and Engineer Computer Ecosystems Through and Beyond Modern Distributed Systems , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[31]  Semih Salihoglu,et al.  Response to “Scale Up or Scale Out for Graph Processing” , 2018, IEEE Internet Computing.

[32]  Charu C. Aggarwal,et al.  Managing and Mining Graph Data , 2010, Managing and Mining Graph Data.